id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-031460-nrxtfl3i	Sharma, Vikas Kumar	Modeling and Forecasting of COVID-19 Growth Curve in India	2020-09-05		.txt	text/plain	6139	368	66	In this article, we analyze the growth pattern of COVID-19 pandemic in India from March 4 to July 11 using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt–Winters models. Further, we search the best-fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and provide the forecast of COVID-19 cases for future days. Ceylan (2020) suggested the use of Auto-Regressive Integrated Moving Average (ARIMA) model to develop and predict the epidemiological trend of COVID-19 for better allocation of resources and proper containment of the virus in Italy, Spain and France. In this article, we first study the growth curve using regression methods (exponential, linear and polynomial etc.) and propose an optimal model for fitting the cases till July 10. In order to find the optimal value of µ, i.e. the turning point between the exponential and polynomial growth, we will use the technique of minimizing the residual sum squares in "Analysis of COVID-19 Cases in India".	./cache/cord-031460-nrxtfl3i.txt	./txt/cord-031460-nrxtfl3i.txt